An interdisciplinary introduction to the field of natural language, emphasizing behavioral, linguistic, and computational perspectives. Topics include language structure, production, comprehension, and acquisition

An interdisciplinary introduction to cognition. Topics covered include learning, memory, attention, concepts and categories, cognitive development, and reasoning, each considered from the perspectives of behavioral study, computational processes, and neural mechanisms.

An introduction to the functioning of the senses and the physiological mechanisms underlying them. Topics include vision, audition, somatosensation, the vestibular system, guestation and olfaction, with an emphasis on the general principles that govern mammalian sensory systems.

An interdisciplinary introduction to perception and action. Topics covered include the perception of motion, depth, surfaces, pattern and object perception, eye movements, motor planning and organization, and attention.

General introduction to neuroscientific studies of various aspects of human cognition and perception, e.g. object-recognition, development, attention, language, vision, etc.
The class will consist both of lectures and also seminar-type discussions led by the students.

Issues of data analysis in experimental research. The course focuses on parametric techniques, specifically analysis of variance. Topics covered include simple and complex designs for between and within subjects factors, including mixed designs; analysis of covariance and trend and contrasts. The course includes a lab in which students are taught to use a popular statistical package for data analysis

This course reviews the leading methods used to investigate cognitive skills and/or their neural substrate in humans. The course is divided into several sections: accuracy and psychophysics; RT and processing states; interference, neighborhood effects and system dynamics; investigations of natural data; brain imaging methods as applied to the cognitive sciences; and issues when studying special populations such as infants, patients, animals or any non-compliant subject. Technical articles on each technique are discussed in combination with specific illustrations of how each has been used to investigate research questions.

This course focuses on: (a) statistical tools that are useful for revealing structure in experimental data; and (b) representation and learning in statistical systems and the implications of these systems for the study of cognitive processes. Examples of the applications of computational methods from the cognitive neuroscience literature are examined throughout the course. Topics covered include: principal component analysis, multi-dimensional scaling, hierarchical and non-hierarchical clustering, regression, classification, time series modeling via hidden Markov models and Kalman filters, Hebbian learning, competitive learning, maximum likelihood estimation, and Bayesian estimation.

Prerequisites: Includes knowledge of calculus. Knowledge of linear algebra and probability theory will also be helpful (though prior knowledge of these areas is not strictly required). Homeworks require students to write computer programs (preferably in MatLab)Last Offered: Spring 2020

The core focus of the course will be on how fMRI can be used to ask questions about neural representations and cognitive and perceptual information processing. Some of the questions that the course will address include: 1) The basic fMRI signal just shows activation in different parts of the brain. How can we get from that to addressing questions about neural representations and neural information processing? 2) Ways of relating neural activation to behavioural performance. Can fMRI provide information over and above what can be obtained from behaviour alone? 3) Standard fMRI analysis using the General Linear Model, including preprocessing steps. 4) Multivariate fMRI analysis using machine learning approaches. There will also be a component, about 20% of the class, on the big-picture aspects of MRI physics and physiology which make fMRI possible.

The first part of the course entails structured laboratory experiments to provide experience with neuroanatomical, neurophysiological and molecular biological approaches to studying neural organization and function. The course concludes with one of two 5-week long research projects that culminate in the production of a final research paper. In one project,students explore laterality in the basal ganglia and its influence on motor behavior. In the other project, students explore the molecular genetics of touch sensation in nematodes.

This course considers how we comprehend the auditory environment. Topics include the physical stimulus for hearing, the physiology of the auditory system (both at the periphery and in the central nervous system), the psychophysics of basic auditory perception (e.g., hearing thresholds), higher level auditory perception (including auditory scene analysis and the perception of complex auditory events such as speech and music), and hearing disorders. Considers research from a diverse range of perspectives including behavioral research, cognitive neuroscience, studies of individual differences, and research that adopts a comparative perspective.

This is a reading seminar that will look at modern research on statistical learning in a number of areas in perception, action and cognition. The course will focus on studies of how the brain adapts to the statistics of both sensory inputs and motor outputs with the goal of finding common conceptual links between diverse behavioral domains, including, sensory adaptation, motor adaptation and learning, visual perception, language processing and various cognitive functions (e.g. causal learning).

Advanced graduate seminar on a chose problem in vision sciences. In previous years, topics have included motion perception, stereopsis, color vision and visuo-motor control. Readings for the course are drawn from the scientific literature in the topic being covered. Students are typically required to lead discussions on papers.

This course provides a hand-on introduction to experimental and analytical methods in cognitive science and artificial intelligence. Each year, it offers three modules from a rotating list, including topics such as brain imaging, computational linguistics, and computer vision. The course is open to graduate students in any discipline. The course is recommended for who intend to pursue research in the the intersection of cognitive science and computer science, but prior experience in those fields is not required.

In this interdisciplinary project course, graduate students will work in mixed teams to develop an artifact that addresses a research question and/or infrastructure need in the intersection of cognitive science and artificial intelligence. Students will learn principles of design by participating in the stages of brainstorming, specification, initial design, prototyping, refinement, and evaluation. The artifacts created by this course could include online showcases, demonstrations, tutorials, blogs, scientific papers, and software components to support further research.

This course is a graduate-level seminar intended to teach students about state-of-the-art probabilistic theories of human cognitive processing. Topics covered include theories of language, perception, categorization, numerical cognition, and decision making.

This seminar will present the fundamentals of information theory with applications to cognitive and neural systems. The course will closely follow textbooks by Cover & Thomas and Li & Vitanyi, aiming to combine mathematical foundations with applications to research. Covered topics will include probability, surprisal, entropy, mutual information, channel capacity, coding theory, and differential entropy. The course will also cover formal measures of complexity, including Kolmogorov complexity and related notions of data compression, minimum description length, and their relationship to learning and inference. Students taking the course for credit or auditing will present papers using these ideas across cognitive science and neuroscience.

Introduces the field of neurochemistry with an emphasis on cellular and molecular neurochemistry. Topics range from study of neurochemical mechanisms that underlie normal neural function to discussion of behavioral disturbances that result from neurochemical abnormalities. Considers neurochemical mechanisms of adaptive behavior, learning and memory, behavioral disorders, gender differences, and drug seeking behavior.

A review of recent progress in computational theories of the brain, emphasizing theories of representation and computation in neural circuits. The course begins with biophysical models of neurons and end with models of complex cognitive functions such as sensory motor transformations or sentence processing

We will discuss the neuroscience and psychology underlying
reward-based decisions. Topics of discussion will include behavioral economics, neuroimaging studies of consumer behavior, physiological studies of the reward system, and computational models of choice and reinforcement learning. Students will be expected to read several scholarly articles each week, attend lectures, and participate in discussions.

Advanced treatment of the development of the nervous system, including the nature/nurture issue and factors that influence the development of neural organization and function. Topics include the production, migration, differentiation and survival of neurons; functional specialization of neural regions; axonal navigation; target mapping. Compares and contrasts developmental plasticity with forms of neural plasticity exhibited in adults.

A survey of the major topics and issues in development. The course covers the development of sensation, perception, cognition, and language in humans, as well as the development of neural mechanisms and systems in other species. A major theme involves the nature/nurture issue, including the interacting roles of experience and maturation, the constraints on plasticity provided by maturation (for example, in critical period phenomena), and the differences and similarities between development and learning.

The course covers a broad range of topics on the child's acquisition of a native language, including literature on the acquisition of spoken and signed languages, as well as theories of the language learning process. Focus is on the acquisition of syntax and morphology.

This seminar in music cognition is intended for graduate students in music theory or cognitive science; others only with permission of instructor. We will survey primary sources in the field on the perception of key, meter, harmony, rhythm, and form. Other topics may include emotion, performance, development, computational modeling, corpus research, and neuroscience. Some topics in experimental methods and statistics will also be covered. Students are required to do a final project and presentation, based on an experiment, computational or corpus research, or a critical survey of a research area.

Covers current and classic topics in the field of language production. Topics include speech error models, computational models of lexical/phonological encoding, issues in syntactic encoding, the incrementality of speech production, comprehension vs. production, and hearer vs. speaker-oriented accounts of production processes.

his seminar offers an in-depth examination of selected topics in language comprehension, including lexical processing, parsing, and anaphora resolution. Theoretical ideas from linguistics and artificial intelligence are integrated with experimental studies of language processing.

An examination of signed languages and the cognitive constraints that shape them, through a detailed consideration of the structure of American Sign Language and other natural signed languages of the world. Includes training in sign language notation and analysis.

Crosslinguistic comparisons among signed languages, considering the possible linguistic universals for signed languages, the degree and types of variation among different signed languages, the ways in which universals and language specific variation for signed languages may compare and contrast to those for spoken languages, and the visual, motoric, and cognitive constraints which may give rise to these phenomena.

Consideration of the processing, historical development, and acquisition of signed languages, with an interest in the ways that language processing, development, and evolution may affect language structure.

This course will explore relationships between musical and linguistic structure. In addition to reading and evaluating early writings on the subject by Bernstein and Lerdahl & Jackendoff, students will assess more recent work by Huron and Patel, and the linguists Hayes and Ladd on prosodic structure. We will also discuss experimental work on prosodic structure in language and on music acquisition in infants. Co-taught by a music theorist and linguist, the course will review basic aspects of phonology, intonational phonology, meter, and memory that are relevant to music. Each student will complete a piece of original research in the form of a term paper and class presentation. Permission of instructor required for non-Eastman students.

A workshop in which students will write a proposal for either a pre-doctoral or post-doctoral NRSA fellowship from NIH. Students will review old NRSA proposals, both successful and unsuccessful and analyze the components of a successful proposal. Through process of peer review and discussion, students will write and revise the main sections of an NRSA proposal, culminating in a penultimate proposal that will be reviewed by two mock study sections – one in the class and one by faculty in BCS and CVS. Reviews from these study sections will be returned a week before the deadline for NRSA proposals at NIH. Students are encouraged to use the class to prepare real proposals that they can submit to NIH.

The purpose of this 1-credit course is to provide first- and second-year graduate students with a set of guiding principles for optimizing their progression through the PhD program. The following topics will be discussed: fulfilling program requirements, advising and mentoring, time management, conference presentations and journal publications, writing skills for journals and grants, how to juggle, persist, drop, and collaborate in your research projects, the post-PhD job market and qualifications required for success.